What is the advantage of using MCTS with value based methods over value based methods only?
Assuming a continuous/uncountable state space, we can only estimate our value function using function approximation, so our estimates will never be true for all states simultaneously (because, loosely speaking, we have far more states than weights). If we can look at the (approximated) value of states we take in, say, 5 actions time, it is better to make a decision based on these estimations, taking into account the true rewards observed after the 5 actions.
Further, MCTS also allows more implicit exploration as when choosing the actions to expand the tree we are potentially choosing lots of non-greedy actions that lead to better future returns.